Continuous 24-h Photoplethysmogram Monitoring Enables Detection of Atrial Fibrillation

Aim: Atrial fibrillation (AF) detection is challenging because it is often asymptomatic and paroxysmal. We evaluated continuous photoplethysmogram (PPG) for signal quality and detection of AF.Methods: PPGs were recorded using a wrist-band device in 173 patients (76 AF, 97 sinus rhythm, SR) for 24 h. Simultaneously recorded 3-lead ambulatory ECG served as control. The recordings were split into 10-, 20-, 30-, and 60-min time-frames. The sensitivity, specificity, and F1-score of AF detection were evaluated for each time-frame. AF alarms were generated to simulate continuous AF monitoring. Sensitivities, specificities, and positive predictive values (PPVs) of the alarms were evaluated. User experiences of PPG and ECG recordings were assessed. The study was registered in the Clinical Trials database (NCT03507335).Results: The quality of PPG signal was better during night-time than in daytime (67.3 ± 22.4% vs. 30.5 ± 19.4%, p < 0.001). The 30-min time-frame yielded the highest F1-score (0.9536), identifying AF correctly in 72/76 AF patients (sensitivity 94.7%), only 3/97 SR patients receiving a false AF diagnosis (specificity 96.9%). The sensitivity and PPV of the simulated AF alarms were 78.2 and 97.2% at night, and 49.3 and 97.0% during the daytime. 82% of patients were willing to use the device at home.Conclusion: PPG wrist-band provided reliable AF identification both during daytime and night-time. The PPG data’s quality was better at night. The positive user experience suggests that wearable PPG devices could be feasible for continuous rhythm monitoring.

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Identification of Transient Noise to Reduce False Detections in Screening for Atrial Fibrillation

Frontiers in Physiology ◽

10.3389/fphys.2021.672875 ◽

2021 ◽

Vol 12 ◽

Author(s):

Hesam Halvaei ◽

Emma Svennberg ◽

Leif Sörnmo ◽

Martin Stridh

Keyword(s):

Atrial Fibrillation ◽

Elderly Population ◽

The Elderly ◽

Low Complexity ◽

Signal Quality ◽

Handheld Device ◽

Expert Review ◽

Sensitivity Specificity ◽

Noise Identification

Screening for atrial fibrillation (AF) with a handheld device for recording the ECG is becoming increasingly popular. The poorer signal quality of such ECGs may lead to false detection of AF, often caused by transient noise. Consequently, the need for expert review in AF screening can become extensive. A convolutional neural network (CNN) is proposed for transient noise identification in AF detection. The network is trained using the events produced by a QRS detector, classified into either true beat detections or false detections. The CNN and a low-complexity AF detector are trained and tested using the StrokeStop I database, containing 30-s ECGs from mass screening for AF in the elderly population. Performance evaluation of the CNN-based quality control using a subset of the database resulted in sensitivity, specificity, and accuracy of 96.4, 96.9, and 96.9%, respectively. By inserting the CNN before the AF detector, the false AF detections were reduced by 22.5% without any loss in sensitivity. The results show that the number of recordings calling for expert review can be significantly reduced thanks to the identification of transient noise. The reduction of false AF detections is directly linked to the time and cost spent on expert review.

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“The surprise questions” using variable time frames in hospitalized patients with advanced cancer

Palliative & Supportive Care ◽

10.1017/s1478951521000766 ◽

2021 ◽

pp. 1-5

Author(s):

Sun Hyun Kim ◽

Sang-Yeon Suh ◽

Seok Joon Yoon ◽

Jeanno Park ◽

Yu Jung Kim ◽

...

Keyword(s):

Palliative Care ◽

Advanced Cancer ◽

Time Frame ◽

Hospitalized Patients ◽

Advanced Cancer Patients ◽

Multicenter Cohort Study ◽

Prediction Of Survival ◽

Sensitivity Specificity ◽

Surprise Question ◽

Time Frames

Abstract Objective Several studies supported the usefulness of “the surprise question” in terms of 1-year mortality of patients. “The surprise question” requires a “Yes” or “No” answer to the question “Would I be surprised if this patient died in [specific time frame].” However, the 1-year time frame is often too long for advanced cancer patients seen by palliative care personnel. “The surprise question” with shorter time frames is needed for decision making. We examined the accuracy of “the surprise question” for 7-day, 21-day, and 42-day survival in hospitalized patients admitted to palliative care units (PCUs). Method This was a prospective multicenter cohort study of 130 adult patients with advanced cancer admitted to 7 hospital-based PCUs in South Korea. The accuracy of “the surprise question” was compared with that of the temporal question for clinician's prediction of survival. Results We analyzed 130 inpatients who died in PCUs during the study period. The median survival was 21.0 days. The sensitivity, specificity, and overall accuracy for the 7-day “the surprise question” were 46.7, 88.7, and 83.9%, respectively. The sensitivity, specificity, and overall accuracy for the 7-day temporal question were 6.7, 98.3, and 87.7%, respectively. The c-indices of the 7-day “the surprise question” and 7-day temporal question were 0.662 (95% CI: 0.539–0.785) and 0.521 (95% CI: 0.464–0.579), respectively. The c-indices of the 42-day “the surprise question” and 42-day temporal question were 0.554 (95% CI: 0.509–0.599) and 0.616 (95% CI: 0.569–0.663), respectively. Significance of results Surprisingly, “the surprise questions” and temporal questions had similar accuracies. The high specificities for the 7-day “the surprise question” and 7- and 21-day temporal question suggest they may be useful to rule in death if positive.

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The Atrial Fibrillation Health Literacy Information Technology Trial: Pilot Trial of a Mobile Health App for Atrial Fibrillation (Preprint)

10.2196/preprints.17162 ◽

2019 ◽

Author(s):

Emily Guhl ◽

Andrew D Althouse ◽

Alexandra M Pusateri ◽

Everlyne Kimani ◽

Michael K Paasche-Orlow ◽

...

Keyword(s):

Quality Of Life ◽

Atrial Fibrillation ◽

Pilot Trial ◽

Intervention Group ◽

Mean Difference ◽

Control Group ◽

Relational Agent ◽

Rhythm Monitoring

BACKGROUND Atrial fibrillation (AF) is a common arrhythmia that adversely affects health-related quality of life (HRQoL). We conducted a pilot trial of individuals with AF using a smartphone to provide a relational agent as well as rhythm monitoring. We employed our pilot to measure acceptability and adherence and to assess its effectiveness in improving HRQoL and adherence. OBJECTIVE This study aims to measure acceptability and adherence and to assess its effectiveness to improve HRQoL and adherence. METHODS Participants were recruited from ambulatory clinics and randomized to a 30-day intervention or usual care. We collected baseline characteristics and conducted baseline and 30-day assessments of HRQoL using the Atrial Fibrillation Effect on Quality of Life (AFEQT) measure and self-reported adherence to anticoagulation. The intervention consisted of a smartphone-based relational agent, which simulates face-to-face counseling and delivered content on AF education, adherence, and symptom monitoring with prompted rhythm monitoring. We compared differences in AFEQT and adherence at 30 days, adjusted for baseline values. We quantified participants’ use and acceptability of the intervention. RESULTS A total of 120 participants were recruited and randomized (59 to control and 61 to intervention) to the pilot trial (mean age 72.1 years, SD 9.10; 62/120, 51.7% women). The control group had a 95% follow-up, and the intervention group had a 93% follow-up. The intervention group demonstrated significantly higher improvement in total AFEQT scores (adjusted mean difference 4.5; 95% CI 0.6-8.3; P=.03) and in daily activity (adjusted mean difference 7.1; 95% CI 1.8-12.4; P=.009) compared with the control between baseline and 30 days. The intervention group showed significantly improved self-reported adherence to anticoagulation therapy at 30 days (intervention 3.5%; control 23.2%; adjusted difference 16.6%; 95% CI 2.8%-30.4%; P<.001). Qualitative assessments of acceptability identified that participants found the relational agent useful, informative, and trustworthy. CONCLUSIONS Individuals randomized to a 30-day smartphone intervention with a relational agent and rhythm monitoring showed significant improvement in HRQoL and adherence. Participants had favorable acceptability of the intervention with both objective use and qualitative assessments of acceptability.

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Recurrence plot and convolutional neural network-based signal quality assessment method for using photoplethysmogram in mobile environment (Preprint)

10.2196/preprints.24054 ◽

2020 ◽

Author(s):

Hangsik Shin

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Quality Assessment ◽

Feature Detection ◽

Layer Structure ◽

Recurrence Plot ◽

Classification Model ◽

Signal Quality ◽

Predictive Values

BACKGROUND In clinical use of photoplethysmogram, waveform distortion due to motion noise or low perfusion may lead to inaccurate analysis and diagnostic results. Therefore, it is necessary to find an appropriate analysis method to evaluate the signal quality of the photoplethysmogram so that its wide use in mobile healthcare can be further increased. OBJECTIVE The purpose of this study was to develop a machine learning model that could accurately evaluate the quality of a photoplethysmogram based on the shape of the photoplethysmogram and the phase relevance in a pulsatile waveform without requiring a complicated pre-processing. Its performance was then verified. METHODS Photoplethysmograms were recorded for 76 participants (5 minutes for each participant). All recorded photoplethysmograms were segmented for each beat to obtain a total of 49,561 pulsatile segments. These pulsatile segments were manually labeled as 'good' and 'bad' classes and converted to a two-dimensional phase space trajectory image with size of 124 × 124 using a recurrence plot. The classification model was implemented using a convolutional neural network with a two-layer structure. It was verified through a five-fold cross validation. RESULTS As a result, the proposed model correctly classified 48,827 segments out of 49,561 segments and misclassified 734 segments, showing a balanced accuracy of 0.975. Sensitivity, specificity, and positive predictive values of the developed model for the test dataset with a ‘bad’ class classification were 0.964, 0.987, and 0.848, respectively. The area under the curve was 0.994. CONCLUSIONS The convolutional neural network model with recurrence plot as input proposed in this study can be used for signal quality assessment as a generalized model with high accuracy through data expansion. It has an advantage in that it does not require a complicated pre-processing or feature detection process. CLINICALTRIAL KCT0002080

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Validation of plasma miR-371a-3p expression in patients with metastatic and early stage germ cell tumor.

Journal of Clinical Oncology ◽

10.1200/jco.2019.37.7_suppl.526 ◽

2019 ◽

Vol 37 (7_suppl) ◽

pp. 526-526 ◽

Cited By ~ 1

Author(s):

Lucia Nappi ◽

Marisa Thi ◽

Amy Lum ◽

David Huntsman ◽

Bernhard J. Eigl ◽

...

Keyword(s):

Germ Cell ◽

Validation Cohort ◽

Early Stage ◽

Clinical Stage ◽

Diagnostic Tools ◽

Rt Pcr ◽

Predictive Values ◽

Sensitivity Specificity ◽

Cell Malignancy

526 Background: Identification of relapsing/residual viable germ cell malignancy (GCM) is often challenging in patients with CSI on surveillance or with residual post-chemotherapy disease. The presence of a biomarker for GCM would overcome the uncertainty of the current methods and improve the quality of care of those patients. Methods: A 2-cohorts pilot study involving patients with clearcut evidence of GCM (clinical stage IS, metastatic and GCM prior orchiectomy) for the development cohort and patients with CSI with or without signs of tumor relapse and patients with metastatic GCM post-chemotherapy for the validation cohort. Blood samples collected in Streck tubes were obtained prior to chemotherapy for the development cohort and post-orchiectomy, at the time of suspicious relapse or post-chemotherapy in the validation cohort. Plasma miR-371a-3p (miR371) was analyzed by RT-PCR. Positive predictive value (PPV), sensitivity, specificity, negative predictive values (NPV) and AUC of the ROC for miR371 and standard diagnostic tools (CT scan, AFP, BHCG and LDH) were calculated correlating qualitative miR371 expression to the presence of viable GCM. Results: 132 patients were enrolled into the development (33 pts) and validation (99 pts) cohorts. Within the development cohort 31/33 pts were miR371 positive, 2/33 pts were negative. 31 true positives were found among the 31 miR371 positive patients for a PPV of 100% (31/31). Two true negatives were found among the 2 patients who had no miR371 expression identified (teratoma, lymphoma). The validation cohort was chosen to evaluate the methodology among patients with predicted lower volumes or no clinically apparent disease. 13/99 patients within the validation cohort were miR371 positive and all 13 had subsequent confirmation of viable GCM. For the overall study of 132 pts, PPV was 100% (46/46), NPV 98%, sensitivity 96% and specificity 100%, the AUC of the ROC was 0.96. Conclusions: Detectable circulating miR-371a-3p levels predict viable GCM and may represent a strategy for biological rather than radiographic assessment for surveillance of early stage and for post-treatment patients. Larger studies to validate these and like results have been planned.

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Evaluating the Human Damage of Tsunami at Each Time Frame in Aggregate Units Based on GPS data

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xli-b8-133-2016 ◽

2016 ◽

Vol XLI-B8 ◽

pp. 133-138

Author(s):

Y. Ogawa ◽

Y. Akiyama ◽

H. Kanasugi ◽

R. Shibasaki ◽

H. Kaneda

Keyword(s):

Population Distribution ◽

Working Hours ◽

Population Data ◽

Time Frame ◽

Regional Level ◽

Distribution Data ◽

Disaster Prevention ◽

Damage Estimation ◽

Night Time ◽

Time Frames

Assessments of the human damage caused by the tsunami are required in order to consider disaster prevention at such a regional level. Hence, there is an increasing need for the assessments of human damage caused by earthquakes. However, damage assessments in japan currently usually rely on static population distribution data, such as statistical night time population data obtained from national census surveys. Therefore, human damage estimation that take into consideration time frames have not been assessed yet. With these backgrounds, the objectives of this study are: to develop a method for estimating the population distribution of the for each time frame, based on location positioning data observed with mass GPS loggers of mobile phones, to use a evacuation and casualties models for evaluating human damage due to the tsunami, and evaluate each time frame by using the data developed in the first objective, and 3) to discuss the factors which cause the differences in human damage for each time frame. By visualizing the results, we clarified the differences in damage depending on time frame, day and area. As this study enables us to assess damage for any time frame in and high resolution, it will be useful to consider provision for various situations when an earthquake may hit, such as during commuting hours or working hours and week day or holiday.

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Evaluating the Human Damage of Tsunami at Each Time Frame in Aggregate Units Based on GPS data

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xli-b8-133-2016 ◽

2016 ◽

Vol XLI-B8 ◽

pp. 133-138 ◽

Cited By ~ 1

Author(s):

Y. Ogawa ◽

Y. Akiyama ◽

H. Kanasugi ◽

R. Shibasaki ◽

H. Kaneda

Keyword(s):

Population Distribution ◽

Working Hours ◽

Population Data ◽

Time Frame ◽

Regional Level ◽

Distribution Data ◽

Disaster Prevention ◽

Damage Estimation ◽

Night Time ◽

Time Frames

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Routine non-thyroid head and neck cytology in a large UK centre: clinical utility and pitfalls

The Journal of Laryngology & Otology ◽

10.1017/s0022215115000092 ◽

2015 ◽

Vol 129 (7) ◽

pp. 682-687

Author(s):

P V Kaye ◽

M Pigera ◽

M M Khan ◽

P Hollows ◽

N Beasley

Keyword(s):

Diagnostic Accuracy ◽

Head And Neck ◽

Reporting System ◽

Needle Aspiration ◽

High Rate ◽

University Hospitals ◽

Predictive Values ◽

The Absolute ◽

Sensitivity Specificity

AbstractObjective:This study aimed to examine the performance of head and neck cytology at Nottingham University Hospitals between 2009 and 2010.Methods:Cases were extracted from the Winpath pathology reporting system and correlations were investigated between results and the histological and clinical outcomes. Specimen adequacy and the sensitivity, specificity, positive and negative predictive values, and diagnostic accuracy of the cytology tests were calculated.Results:In all, 19.7 per cent of aspirates were judged to be inadequate. The absolute and relative sensitivities of head and neck cytology were 87.0 per cent and 89.0 per cent, respectively, and the absolute and relative specificities were 99.0 per cent and 97.0 per cent, respectively. The positive predictive values were 99.0 per cent and 96.0 per cent and the negative predictive values were 92.0 per cent and 92.0 per cent for a diagnostic accuracy of 94.5 per cent and 93.0 per cent. The performance was consistent with previous reports and superior to that of a recent UK series. The high rate of inadequate samples is, however, a concern.Conclusion:Head and neck cytology is a robust technique at our institution, although there are certain problem areas. There is room for improvement in the technical quality of fine needle aspiration.

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The Atrial Fibrillation Health Literacy Information Technology Trial: Pilot Trial of a Mobile Health App for Atrial Fibrillation

JMIR Cardio ◽

10.2196/17162 ◽

2020 ◽

Vol 4 (1) ◽

pp. e17162

Author(s):

Emily Guhl ◽

Andrew D Althouse ◽

Alexandra M Pusateri ◽

Everlyne Kimani ◽

Michael K Paasche-Orlow ◽

...

Keyword(s):

Quality Of Life ◽

Atrial Fibrillation ◽

Pilot Trial ◽

Intervention Group ◽

Mean Difference ◽

Control Group ◽

Relational Agent ◽

Rhythm Monitoring

Background Atrial fibrillation (AF) is a common arrhythmia that adversely affects health-related quality of life (HRQoL). We conducted a pilot trial of individuals with AF using a smartphone to provide a relational agent as well as rhythm monitoring. We employed our pilot to measure acceptability and adherence and to assess its effectiveness in improving HRQoL and adherence. Objective This study aims to measure acceptability and adherence and to assess its effectiveness to improve HRQoL and adherence. Methods Participants were recruited from ambulatory clinics and randomized to a 30-day intervention or usual care. We collected baseline characteristics and conducted baseline and 30-day assessments of HRQoL using the Atrial Fibrillation Effect on Quality of Life (AFEQT) measure and self-reported adherence to anticoagulation. The intervention consisted of a smartphone-based relational agent, which simulates face-to-face counseling and delivered content on AF education, adherence, and symptom monitoring with prompted rhythm monitoring. We compared differences in AFEQT and adherence at 30 days, adjusted for baseline values. We quantified participants’ use and acceptability of the intervention. Results A total of 120 participants were recruited and randomized (59 to control and 61 to intervention) to the pilot trial (mean age 72.1 years, SD 9.10; 62/120, 51.7% women). The control group had a 95% follow-up, and the intervention group had a 93% follow-up. The intervention group demonstrated significantly higher improvement in total AFEQT scores (adjusted mean difference 4.5; 95% CI 0.6-8.3; P=.03) and in daily activity (adjusted mean difference 7.1; 95% CI 1.8-12.4; P=.009) compared with the control between baseline and 30 days. The intervention group showed significantly improved self-reported adherence to anticoagulation therapy at 30 days (intervention 3.5%; control 23.2%; adjusted difference 16.6%; 95% CI 2.8%-30.4%; P<.001). Qualitative assessments of acceptability identified that participants found the relational agent useful, informative, and trustworthy. Conclusions Individuals randomized to a 30-day smartphone intervention with a relational agent and rhythm monitoring showed significant improvement in HRQoL and adherence. Participants had favorable acceptability of the intervention with both objective use and qualitative assessments of acceptability.

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Patient-Specific Detection of Atrial Fibrillation in Segments of ECG Signals using Deep Neural Networks

Ciencia e Ingeniería Neogranadina ◽

10.18359/rcin.4156 ◽

2019 ◽

Vol 30 (1) ◽

pp. 45-58 ◽

Cited By ~ 4

Author(s):

Jeyson A. Castillo ◽

Yenny C. Granados ◽

Carlos Augusto Fajardo Ariza

Keyword(s):

Atrial Fibrillation ◽

Computational Cost ◽

Patient Specific ◽

Specific Patient ◽

Final Model ◽

Ecg Signals ◽

Sensitivity Specificity ◽

Memory Resources

Atrial Fibrillation (AF) is the most common cardiac arrhythmia worldwide. It is associated with reduced quality of life and increases the risk of stroke and myocardial infarction. Unfortunately, many cases of AF are asymptomatic and undiagnosed, which increases the risk for the patients. Due to its paroxysmal nature, the detection of AF requires the evaluation, by a cardiologist, of long-term ECG signals. In Colombia, it is difficult to have access to an early diagnosis of AF because of the associated costs to the detection and the geographical distribution of cardiologists. This work is part of a macro project that aims to develop a specific-patient portable device for the detection of AF. This device will be based on a Convolutional Neural Network (CNN). We aim to find a suitable CNN model, which later could be implemented in hardware. Diverse techniques were applied to improve the answer regarding accuracy, sensitivity, specificity, and precision. The final model achieves an accuracy of , a specificity of , a sensitivity of and a precision of . During the development of the model, the computational cost and memory resources were taking into account in order to obtain an efficient hardware model in a future implementation of the device.

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